The Roles of School Readiness and Poverty-Related Risk for 6th Grade Outcomes
Bibliographic record
Abstract
Low-income students are at increased risk for grade retention and suspension, which dampens their chances of high school graduation, college attendance, and future success. Drawing from a sample of 357 children and their families who participated in the Chicago School Readiness Project, we examine whether greater exposure to cumulative poverty-related risk from preschool through 5th grade is associated with greater risk of student retention and suspension in 6th grade. Logistic regression results indicate that exposure to higher levels of cumulative risk across the elementary school years is associated with students’ increased risk of retention in 6th grade, even after controlling for child school readiness skills and other covariates. Importantly, findings of the association between average cumulative risk exposure and student suspension are more complex; the role of poverty-related risk is reduced to non-significance once early indicators of child school readiness and other covariates are included in regression models. While, children’s early externalizing behavior prior to kindergarten places children at greater risk of suspension 7 years later, children’s higher levels of internalizing behaviors and early math skills are associated with significantly decreased risk of suspension in the 6th grade. Together, findings from the study suggest the complex ways that both early school readiness and subsequent exposure to poverty-related risk may both serve as compelling predictors of children’s likelihood of “staying on track” academically in the 6th grade.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".